<html><head><title>Machine Learning Engineer - San Francisco, CA</title></head>
<body><h2>Machine Learning Engineer - San Francisco, CA</h2>
*****************************************************
Help the world spend their time with intention.
*****************************************************

Ping builds automation tools for professional services. We're starting by automating timekeeping for the world's largest enterprises and then using that data to transform the way these industries price, operate and work. Professionals won't just use our system to manage their time but to create it.

And we need your help.

****************
Our Vision
****************

We believe that time is the most valuable thing we can spend. We also believe that professionals are wasting a staggering amount of time at work because they have no idea where they are spending it. Previously, professionals had to manually record every six-minutes of their day into antiquated timekeeping software. We are automating this workflow so professionals can concentrate on creating value, not being administrators.

And that's just the beginning. We will then leverage the data we collect to optimize the way work is priced and performed across these trillion-dollar industries. If done correctly, we'll allow professionals to output in 4 hours what now takes them 10. This gives you an opportunity to impact the world at scale - give people back time they'd otherwise lose forever and empower them to spend it with intention.

***************************************
Your Roles & Responsibilities
***************************************

We're looking for fearless, creative, and talented visionaries who are passionate about creating a first-of-its-kind product, solving complex technical problems, and working in an entrepreneurial environment. For our ML Engineers, this presents the unique opportunity of scaling Ping's machine learning system:


<li>Explore and visualize data to understand its quality; identifying differences that could affect performance when deploying the model in production</li><li>Build powerful machine learning models, including feature engineering, model selection, and parameter tuning; create solutions that balance the need for accuracy with the need for security and privacy of customer data</li><li>Deploy those models and monitor performance via user behavior over time</li><li>Apply system-level strategic thinking; analyze how our machine learning models can adapt to insights from new data sources and identify creative ways to expand those datasets</li><li>Scale machine learning infrastructure, such as model management for an online model training pipeline</li><li>Coordinate and align with the software engineering, product, and design teams on data flow, user requirements, and UI/UX decisions</li>
******************
Requirements
******************

We heavily emphasize rapid growth and development for our team members. The ability to learn and iterate quickly is valued as much as the skillset you come in with. The ideal candidate would have:


<li>Experience with handling and formatting large quantities of raw, unstructured, and noisy text data using libraries such as NumPy, pandas, nltk, and spaCy</li><li>Proficiency in developing and building models in Python with libraries such as Scikit-learn, Keras, and XGBoost</li><li>Experience deploying models in production at scale (e.g., via Flask/WSGI server)</li><li>Familiarity with engineering best practices such as unit testing, continuous integration, and version control</li><li>The ability to build and deploy quick demos and prototypes via web or native apps</li><li>Excellent communication skills</li>
Nice-to-haves include:

<li>PhD or Masters degree in a related field of study (e.g., Computer Science, Linguistics, Statistics, Mathematics), or equivalent professional experience</li><li>Experience with natural language processing (NLP) and natural language generation (NLG) tasks, such as text classification, named entity recognition (NER), and document summarization</li><li>Proficiency in a deep learning framework such as TensorFlow, PyTorch, or Caffe, especially with the use of word embeddings or language models (e.g., ULMFiT, BERT)</li>
***********
Our Company
***********

Ping is an AI company automating time and billing for professional services, beginning with enterprise law firms. Ping will then use the data it collects to unlock a limitless opportunity to power pricing, efficiency, and build automation tools across all the industries we serve.


<li>Traction — double-digits in enterprise clients, including some of the largest law firms in the world</li><li>Value — Our clients are currently experiencing an 11% revenue lift</li><li>Recognition — Ping was awarded the Legal Tech Startup of the Year in 2017 by the American Bar Association and recently named in The National Law Journal's list of Top Emerging Legal Technologies</li><li>Team — The team (~20 and doubling) is almost exclusively product-focused, comprised mainly of AI and full-stack engineers</li>
***********
Our Funding
***********

Raised a large, preemptive Series A this June (not yet public) from one of the top Series A investors in the world.

Raised a seed round last year, which was led by First Round Capital ( https://firstround.com/ ) and followed on by Initialized ( https://initialized.com/ ), BoxGroup ( http://boxgroup.com/ ), Ulu Ventures ( https://www.uluventures.com/ ), and The House Fund ( https://thehouse.fund/ ).

We are backed by the best of the best who are committed to seeing Ping become their next success story.

**************

Other Details:
**************

At Ping, we welcome diverse perspectives and people who think rigorously and aren't afraid to challenge assumptions.

Ping is an equal opportunity employer and we celebrate diversity and are committed to creating an inclusive environment for all employees.

Join us on our mission to help solve time.</body>
</html>